Bibliographic record
Abstract
Relatively recently, a new negator, “Abi...ni”, emerged in the conversational language of the younger generation of Yoruba speakers. This new linguistic form is termed in this paper as “splitting negator”, owing to the observation that it consists of two particles that are structurally circumfixed with a positive statement. The paper therefore attempts a syntactic cum semantic analysis of this lexical item in order to ascertain whether or not it should be “officially” admitted into the Yoruba lexicon. The data analysed in this study were obtained via researcher’s observation and supplemented by introspective method since the researcher also belongs to the social class of speakers who predominantly use the phenomenon under investigation. Among others, the study fundamentally establishes that this splitting negator is idiomatic as an isolated form and that it often expresses pragmatic ambiguity when it is used in discourse such that it is the context of use that normally determines its interpretation. The paper concludes by proposing that “Abi...ni” be granted linguistic license as a negator in Yoruba, as this will not only encourage lexicon expansion but will also serve as a new stylistic medium of expressing the opposite of a positive statement in the language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".